The overwhelming majority of eclipsing white dwarf (WD) binary systems show quasi-periodic variations in eclipse timings on many year time-scales. Currently, the mechanism behind these eclipse time variations (ETVs) is not known, with the main competing theories being the planetary hypothesis and the Applegate/Lanza mechanisms. Here, we present a

Context. Detection and characterisation of Jovian analogues in precision radial velocity (RV) measurements is gaining momentum due to the constantly increasing observational baseline of Doppler surveys. The occurrence rate of Jovian-mass exoplanets is crucial to understanding the architecture of planetary systems. However, long-period RV signals in

Context. Understanding how the large-scale kinematics of the Milky Way (MW) shape the formation and evolution of the interstellar medium remains challenging from an observational perspective, and numerical models that can reproduce the observed structure and kinematics of the MW are much needed to be able to infer how the MW might work as a star

Context. Different modes of oscillations are frequently observed in solar prominences, and prominence seismology helps estimate important physical parameters such as the magnetic field strength. Although the simultaneous detection of longitudinal and transverse oscillations in the same filament is not common, such rare observations provide a unique

Context. The James Webb Space Telescope (JWST) enables the identification of quiescent galaxies out to early epochs, offering a transformative view of their evolution. However, the photometric selection of quiescent galaxy candidates (QGCs) and the derivation of their key physical quantities, such as stellar masses (M ★) and dust attenuation

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200 000 DESI spectra comprising galaxies, quasars and stars. We